Hardware Debugging & Optimization: Debug, profile, and optimize low-level robot control loop performance, addressreal-world latency, and solve sim-to-real gaps directly on the hardware.
Simulation Support (Secondary): Utilizesimulation tools (e.g., Isaac Sim/Gazebo) as needed for basic policy trainingor initial behavioral testing before full-scale real-world deployment.
Education: Bachelor’s degree orhigher in Robotics, Computer Science, Computer Engineering, MechanicalEngineering, or a related technical discipline. (Strong personalportfolio/projects will be highly valued).
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Working knowledge of computer vision and deep learning inference concepts (pipelines, tensors, common CV tasks, latency/accuracy tradeoffs). You do not need to be a model developer/researcher, but must be fluent in deploying and running models.
Experience optimizing inference for edge hardware (NPUs/MPUs/GPUs/accelerators), including quantization and runtime constraints.
Design and implement model evaluation, monitoring and optimise performance of in-house developed AI solutions through pre-training and post-training techniques.
Optimise model inference performance and resource utilisation.
Develop and deploy risk mitigation measures such as guardrails to ensure safe and responsible use of AI products, aligning with organisational standards and regulatory requirements.
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Work directly with customers to understand their business, uncover the real problem behind a request, and turn it into a product that creates measurable value.
Use AI coding agents and modern development workflows to improve your own speed, quality, and ability to deliver.
You have 4+ years of professional software engineering experience and strong full-stack fundamentals.
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